Dynamic dialysis is one of the most common methods for the determination of release kinetics from nanoparticle drug delivery systems. Drug appearance in the "sink" receiver compartment is a consequence of release from the nanoparticles into the dialysis chamber followed by diffusion across the dialysis membrane. This dual barrier nature inherent in the method complicates data interpretation and may lead to incorrect conclusions regarding nanoparticle release half-lives. Although the need to consider the barrier properties of the dialysis membrane has long been recognized, there is insufficient quantitative appreciation for the role of the driving force for drug transport across that membrane. Reversible nanocarrier binding of the released drug reduces the driving force for drug transport across the dialysis membrane leading to a slower overall apparent release rate. This may lead to the conclusion that a given nanoparticle system will provide a sustained release in vivo when it will not. This study demonstrates these phenomena using model lipophilic drug-loaded liposomes varying in lipid composition to provide variations in bilayer permeability and membrane binding affinities. Model simulations of liposomal transport as measured by dynamic dialysis were conducted to illustrate the interplay between the liposome concentration, membrane/water partition coefficient, and the apparent release rate. Reliable determination of intrinsic liposomal bilayer permeability coefficients for lipophilic drugs by dynamic dialysis requires validation of drug release kinetics at varying nanoparticle concentration and the determination of membrane binding coefficients along with appropriate mechanism-based mathematical modeling to ensure the reliability and proper interpretation of the data.

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Abstract:The medium-range production scheduling problem of a multi-product batch plant is studied. The methodology consists of a decomposition of the whole scheduling period to successive short horizons. A mathematical model is proposed to determine each short horizon and the products to be included. Then a novel continuous-time formulation for short-term scheduling of batch processes with multiple intermediate due dates is applied to each time horizon selected, leading to a large-scale mixed-integer linear programming (MILP) problem. Special structures of the problem are further exploited to improve the computational performance. An integrated graphical user interface implementing the proposed optimization framework is presented. The effectiveness of the proposed approach is illustrated with a large-scale industrial case study that features the production of thirty five different products according to a basic 3-stage recipe and its variations by sharing ten pieces of equipment.

This work explores the utility of the cynomolgus monkey as a preclinical model to predict hepatic uptake clearance mediated by organic anion transporting polypeptide (OATP) transporters. Nine OATP substrates (rosuvastatin, pravastatin, repaglinide, fexofenadine, cerivastatin, telmisartan, pitavastatin, bosentan, and valsartan) were investigated in plated cynomolgus monkey and human hepatocytes. Total uptake clearance and passive diffusion were measured in vitro from initial rates in the absence and presence of the OATP inhibitor rifamycin SV , respectively. Total uptake clearance values in plated hepatocytes ranged over three orders of magnitude in both species, with a similar rank order and good agreement in the relative contribution of active transport to total uptake between cynomolgus monkey and human. In vivo hepatic clearance for these nine drugs was determined in cynomolgus monkey after intravenous dosing. Hepatic clearances showed a range similar to human parameters and good predictions from respective hepatocyte parameters (with 2.7- and 3.8-fold bias on average, respectively). The use of cross-species empirical scaling factors (determined from cynomolgus monkey data either as the data set average or individual drug values) improved prediction (less bias, better concordance) of human hepatic clearance from human hepatocyte data alone. In vitro intracellular binding in hepatocytes also correlated well between species. It is concluded that the minimal species differences observed for the current data set between cynomolgus monkey and human hepatocyte uptake, both in vitro and in vivo, support future use of this preclinical model to delineate drug hepatic uptake and enable prediction of human in vivo intrinsic hepatic clearance.

We aim to establish an in vivo preclinical model to enable simultaneous assessment of inhibition potential of an investigational drug on clinically relevant drug transporters, organic anion-transporting polypeptide (OATP)1B, breast cancer resistance protein (BCRP), P-glycoprotein (P-gp), and organic anion transporter (OAT)3. Pharmacokinetics of substrate cocktail consisting of pitavastatin (OATP1B substrate), rosuvastatin (OATP1B/BCRP/OAT3), sulfasalazine (BCRP), and talinolol (P-gp) were obtained in cynomolgus monkey-alone or in combination with transporter inhibitors. Single-dose rifampicin (30 mg/kg) significantly ( < 0.01) increased the plasma exposure of all four drugs, with a marked effect on pitavastatin and rosuvastatin [area under the plasma concentration-time curve (AUC) ratio ∼21-39]. Elacridar, BCRP/P-gp inhibitor, increased the AUC of sulfasalazine, talinolol, as well as rosuvastatin and pitavastatin. An OAT1/3 inhibitor (probenecid) significantly ( < 0.05) impacted the renal clearance of rosuvastatin (∼8-fold). In vitro, rifampicin (10 M) inhibited uptake of pitavastatin, rosuvastatin, and sulfasalazine by monkey and human primary hepatocytes. Transport studies using membrane vesicles suggested that all probe substrates, except talinolol, are transported by cynoBCRP, whereas talinolol is a cynoP-gp substrate. Elacridar and rifampicin inhibited both cynoBCRP and cynoP-gp in vitro, indicating potential for in vivo intestinal efflux inhibition. In conclusion, a probe substrate cocktail was validated to simultaneously evaluate perpetrator impact on multiple clinically relevant transporters using the cynomolgus monkey. The results support the use of the cynomolgus monkey as a model that could enable drug-drug interaction risk assessment, before advancing a new molecular entity into clinical development, as well as providing mechanistic insights on transporter-mediated interactions.